Arrangement and method for finding out the number of sources of partial discharges

ABSTRACT

A method and an arrangement ( 300, 400 ) for separating partial discharge pulses originating from various partial discharge sources in an electric system, comprises the steps of measuring a variable of the electric system, such as voltage or current, to which partial discharges occurring in the electric system cause pulses, separating the pulses caused by partial discharges, i.e. partial discharge pulses, and occurring in the measured variable, defining and storing pulse parameters depicting the partial discharge pulses, or information from which the pulse parameters can be derived, defining ( 301 ) one or more characteristic parameters depicting the properties of partial discharge pulses for each partial discharge pulse by means of the pulse parameters after at least a predefined number of partial discharge pulses has been obtained, and separating ( 302 ) on the basis of one or more values of characteristic parameters the partial discharge pulses originating from different partial discharge sources into pulse groups. In addition, the number of the partial discharge sources can be defined ( 305 ) on the basis of the number of the pulse groups.

BACKGROUND OF THE INVENTION

The invention relates to a method of s partial discharge pulsesoriginating from various partial discharge sources in an electric systemand preferably of determining the number of the partial dischargesources.

The voltage strength of an insulating structure refers to its ability toendure voltage stress without electric discharges that causedisturbances or damage. If the voltage stress in an the insulatingstructure is increased sufficiently, discharges occur which make theinsulation completely or partially conductive. The latter are calledpartial discharges. A partial discharge does not unite the electrodesand, thus, the insulating properties of the insulating material do notcompletely disappear. Partial discharges do, however, wear theinsulating material and thus further weaken its voltage strength and mayfinally lead to a complete electric discharge. Partial discharges can bedivided into two main groups, internal and external discharges. Internaldischarges comprise cavity discharges and external discharges comprisesurface, corona and spark discharges. Each group can further be dividedinto several subgroups which are often difficult to clearly distinguishfrom each other. Partial discharge pulses are very fast pulses andusually occur as pulse groups. A partial discharge and the reversal ofcharge that occurs in connection with it show as a current pulse in theconnectors of the insulating material. In practice, these current pulsesalso sum into the phase voltage of the system.

There are several known solutions for detecting partial discharges in anelectric system. Partial discharges can, for instance, be detected bymeans of an electric measurement, acoustically by the sound theygenerate or on the basis of the electromagnetic radiation they produce.To detect the existence of partial discharges, it is also useful todistinguish from each other the partial discharge pulses originatingfrom different partial discharge sources. If the partial dischargepulses originating from different sources can be classified into theirown groups, identifying the cause of the partial discharge becomeseasier, as the partial discharge sources can be analysed one at a timeand it is also possible to determine the number of the partial dischargesources. Partial discharges can be generated simultaneously in severalpartial discharge sources, but known solutions for detecting partialdischarges usually cannot distinguish from each other partial dischargescoming from different sources.

BRIEF DESCRIPTION OF THE INVENTION

It is thus an object of the invention to develop a method and a systemimplementing the method so as to solve the above-mentioned problems. Theobject of the invention is achieved by a method and system characterizedin what is stated in the independent claims 1 and 9. Preferredembodiments of the invention are disclosed in the dependent claims.

The invention is based on classifying partial discharge pulsesoriginating from different partial discharge sources in an electricsystem into pulse groups on the basis of one or more characteristicparameters defined from them. In addition, the number of the partialdischarge sources can be defined by means of the pulse groups.

The method and system of the invention provide the advantage that theydo not require any training phase performed in advance, but allow thedistinguishing from each other of partial discharge pulses originatingfor instance from different partial discharge sources of different typesor at various distances to the extent that they differ from each otherwithin the scope of the used characteristic parameters. In addition, themethod and system of the invention facilitate the identification of thecause of a partial discharge by enabling the analysis of partialdischarge sources one at a time and also enable the automaticdetermination of the number of the partial discharge sources.

BRIEF DESCRIPTION OF THE FIGURES

In the following, the invention will be described by means of preferredembodiments and with reference to the attached drawings in which

FIG. 1 shows a block diagram of a method of eliminating narrow-bandinterference,

FIG. 2 shows an interfering exemplary signal in a time domain,

FIG. 3 shows an amplitude spectrum of the exemplary signal,

FIG. 4 shows the amplitude spectrum of the exemplary signal scaled inrelation to the amplitude,

FIG. 5 shows sectional medians of the amplitude spectrum and an envelopeadapted to them,

FIG. 6 shows an adjusted amplitude spectrum and a cut level of highpeaks,

FIG. 7 shows a corrected adjusted amplitude spectrum and a cut level oflow peaks,

FIG. 8 shows the amplitude spectrum calculated from the correctedspectrum,

FIG. 9 shows the corrected exemplary signal in a time domain,

FIG. 10 shows the amplitude distribution of the adjusted amplitudespectrum,

FIG. 11 shows a block diagram of a method of eliminating asynchronousimpulse interference,

FIG. 12 shows the division of an amplitude area,

FIG. 13 shows the basic structure of a time difference matrix,

FIG. 14 shows an interfering exemplary signal,

FIG. 15 shows the peaks detected in the exemplary signal,

FIG. 16 shows the distribution of the blocks of the time differencedistribution,

FIG. 17 shows a time difference distribution formed for the exemplarysignal,

FIG. 18 shows the interfering pulses detected in the exemplary signal,

FIG. 19 shows an exemplary signal from which interfering pulses havebeen eliminated,

FIG. 20 shows an example of a time difference matrix,

FIG. 21 shows a block diagram of a method of eliminating synchronousimpulse interference,

FIG. 22 shows a block diagram of a system of the invention according toan embodiment thereof,

FIG. 23 shows a block diagram of a sampling, measuring signal filteringand partial discharge pulse collection block according to an embodimentthereof,

FIG. 24 shows an example of a rise time histogram, and

FIG. 25 shows an example of a filtered rise time histogram.

DETAILED DESCRIPTION OF THE INVENTION

A block diagram of a method and system of the invention is shown in FIG.22. The invention can be divided into two main blocks: sampling,measuring signal filtering and partial discharge pulse collection 400and partial discharge pulse analysis 300. FIG. 22 also shows theoperation of the analysis block 300 illustrated by means of a flowchart. The use of the method and arrangement of the invention is notlimited to any specific system, but they can be used in connection withvarious electric systems, such as electric networks or apparatuses, todistinguish from each other any partial discharge pulses originatingfrom different partial discharge sources and also to determine thenumber of the partial discharge sources. The arrangement of theinvention can be implemented by means of digital signal processingequipment, for instance.

FIG. 23 shows a flow chart illustrating the operation of the sampling,measuring signal filtering and partial discharge pulse collection block400 according to an embodiment thereof. In the method, a high-frequencyvoltage (or another variable from which partial discharge pulses can bedistinguished) occurring in an electric network is preferablycontinuously digitised during several network cycles. The usedmeasurement method bears no significance to the basic idea of theinvention and depends, for instance, on the electric system beingexamined. The lower limiting frequency f_(l) is a few tens of kilohertz,for instance, and the upper limiting frequency f_(u) is severalmegahertz, for instance (e.g. f_(l)≈60 kHz and f_(u)≈8 MHz). Thedigitising results 130 at a sampling frequency of 16 MS/s, for instance,in a total of 960,000 samples during three network cycles. Thisthree-cycle packet is in the following called a measurement and it isalso the input data of an algorithm. The algorithm preferably eliminates100, 121, 200 interfering signals from the measurement using digitalfiltering, for instance. The algorithm picks partial discharge pulsesfrom the sample string remaining in the measurement. From the pulses,the algorithm calculates 111 for instance the following parametersdepicting the pulse form: rise time of the pulse (preferably 10 to 90%points), fall time of the pulse (preferably 90 to 10% points), width ofthe pulse (preferably at a height of 50%). Which pulse parameters aredefined at this stage depends on the characteristic parameters to beused later in the analysis stage. The above-mentioned pulse parametersare stored 120 for each pulse for analysis.

Alternatively, it is possible to store the following information, forinstance, on each pulse: 50 to 100 sample points of each pulse, thestarting phase angle of the pulse, the sequence number of the networkcycle (where the pulse occurred) and the time stamp of the starting timeof the network cycle (where the pulse occurred). In this alternativecase, the pulse parameters mentioned earlier are calculated only in theanalysis block 300.

It should be noted that the filtering of the measuring signal and thecollection of the partial discharge pulses can also be performedutilising other methods without this having any significance withrespect to the basic idea of the invention. It is also possible thatonly a part of the presented filtering methods are used or that nofiltering is needed, if the system being examined is protected againstexternal interference.

One interference type related to partial discharge measurements isnarrow-band interference. Narrow-band interference refers to interferingsignals whose spectrum is narrow, i.e. the energy of the signal isconcentrated on a narrow frequency range. Various radio transmitters andcommunications equipment operating on a narrow frequency range typicallycause narrow-band interference. This type of interference may mask weakpartial discharge pulses, thus weakening the sensitivity of the partialdischarge measurement. Another interference type related to partialdischarge measurements is asynchronous impulse interference which is apulse-form interference and does not occur synchronously with a phasevoltage (nominal frequency, e.g. 50 or 60 Hz), in other words, inconsecutive phase voltage cycles, the pulses do not occur at the samephase angles. The time between consecutive interfering pulses remainsalmost constant, however. Commutating pulses of an inverter are atypical example of asynchronous impulse interference. A thirdinterference type related to partial discharge measurements issynchronous impulse interference which is a pulse-form interference andoccurs synchronously with a phase voltage. Interfering pulses repeat inconsecutive cycles at nearly constant phase angles. In addition, theamplitude of the pulses remains nearly constant. Synchronous impulseinterference is caused by commutating pulses of rectifiers and phaseangle regulation, for instance.

Eliminating narrow-band interference 100

Narrow-band interference is shown as peaks in the amplitude spectrum.The width of the peak is directly proportional to the width of theinterference band. To be able to eliminate narrow-band interference froma signal, one must be able to identify from the amplitude spectrum anypeaks occurring in it. The power of partial discharges and noise isevenly distributed along the entire frequency range of the spectrum. Theamplitude spectrum of white noise is according to its specificationconstant on the entire frequency range. With coloured noises, the poweris higher on some frequency ranges, but even these cases show as planarareas in the amplitude spectrum. In the tests performed with themeasuring system used as an example in the application, the applicanthas noticed that the power of partial discharges is distributed alongthe entire frequency range in the spectrum. Partial discharges do,however, have more power at low frequencies than at high frequencies.For instance FIG. 4, which discloses the amplitude spectrum |G(jω)| ofthe exemplary signal G(jω) scaled in relation to the amplitude, showsthat the power of the partial discharges is higher at the frequencyrange 0 to 2.5 MHz and at the frequency range 2.5 to 8 MHz, the power isnearly constant. The peaks shown in FIG. 4 are caused by narrow-bandinterference. Narrow-band interference has in the example of FIG. 4concentrated on low frequencies, but in practice, they may occur on theentire frequency range.

A block diagram illustrating a method of eliminating narrow-bandinterference is shown in FIG. 1. The method is based on modifying 110 afinite-length signal in a frequency domain. For this purpose, before thesignal is modified, it is transformed 101 from a time domain (g(t),where t is time) to a frequency domain (G(jω), where j is an imaginaryunit and ω=2πf, where f is the frequency) in time intervals of suitablelength, preferably by Fourier transformation. After the time interval ofthe signal has been modified, it is returned 109 to the time domain by areverse Fourier transformation. The examples use a 60 ms-long signaltime interval at a 16 MHz sampling frequency, whereby a total of 960,000sample points are obtained. The length of the signal time interval maydiffer from the above exemplary value.

FIG. 2 shows the exemplary signal g(t) in a time domain, wherein thevertical axis depicts the signal amplitude g(t) and horizontal axis thetime t. It should be noted that the graphs shown in FIGS. 2 to 10 onlydescribe one exemplary signal and they are intended only to illustratethe operation of a filtering method. The amplitude spectrum |G(jω)|(FIG. 3), which depicts the signal amplitude |G(jω)| (vertical axis) inrelation to the frequency f (horizontal axis), is calculated 102 fromthe Fourier spectrum of the signal transformed into the frequencydomain, i.e. the spectrum G(jω). FIG. 4 shows the amplitude spectrum|G(jω)| scaled in relation to the amplitude. If one tries to identifythe peaks of narrow-band interference from an amplitude spectrum |G(jω)|like that of FIG. 4, it may happen that a strong power peak of a partialdischarge occurring in the frequency range 0 to 1 MHz, for instance, isinterpreted as an interference peak. If, due to this, the frequencyrange 0 to 1 MHz is filtered away completely, a considerable amount ofpower is removed from the partial discharge signal. This results in thedistortion of partial discharge pulses and the filtering result cannotbe used. To avoid the above problem, the amplitude spectrum |G(jω)| ofthe signal is adjusted. The adjustment is done by finding the envelopeof a uniform bottom level of the amplitude spectrum. In the case of FIG.4, the uniform area at the bottom of the amplitude spectrum can beconsidered the bottom level. Peaks do not belong to the bottom level.For defining the bottom level envelope of the amplitude spectrum, theamplitude spectrum is divided into sections, for instance 32 sections,and a median is defined for each section. The first of the 32 sectionalmedians is preferably left out, if in sampling, the measuring signal washigh-pass filtered to remove the main voltage and harmonics, in whichcase the median of the first section does not represent the actual formof the envelope. The envelope is obtained 103 by matching to these 31points (circled values in FIG. 5) for instance an exponential thirddegree polynome 51 of form

envelope=e ^(ax) ³ ^(+bx) ² ^(+cx+d),

wherein

a, b, c and d=coefficients of the polynome,

e=Neper number, and

x=frequency.

The amplitude spectrum |G(jω)| is adjusted 104 by dividing it by theenvelope values sample by sample. The adjusted amplitude spectrum|G(jω)|_(o) is shown in FIG. 6. In the adjusted amplitude spectrum, thebottom level is nearly constant and the peaks rising from it arenarrow-band interference. Narrow-band interference peaks are easy toidentify in an adjusted amplitude spectrum, if a bottom level has beendefined. A simple solution would be to use a fixed cut level. Thestrength and density of partial discharges and the power of thebackground noise, and thus also the power of the entire signal, vary,however, thus also making the height of the bottom level of theamplitude spectrum to vary. When using a fixed cut level, the levelshould be set so high that one can be absolutely sure of notinterpreting the bottom level, and, at the same time, the partialdischarges, as interference. In this case, however, the sensitivity ofinterference elimination decreases, i.e. some of the interference is noteliminated. The most advantageous solution is to define the cut levelcase by case. The cut level must be set as low as possible, but clearlyabove the bottom level, however. An optimal cut level can be defined bymeans of the average and standard deviation or variance of an adjustedamplitude spectrum, for instance. The amplitude distribution of theadjusted amplitude spectrum is shown in FIG. 10, wherein the horizontalaxis shows the amplitude value and the vertical axis the probabilitydensity of the amplitude value. The distribution resembles an χ²distribution. There is a formula for an χ² distribution, as there is onefor a normal distribution, by means of which it is possible to estimatehow large a part of the values is within the given limits. For instance,of the values of a normally distributed signal, 95% is within the limitsμ±1,96σ (μ is the average and σ is the standard deviation). The formulaof the χ² distribution is in the same form as that of the normaldistribution, but the probability limits differ. The cut level level ofthe spectrum peaks can be defined 105 and 107 by means of the averageand standard deviation of the adjusted amplitude spectrum, for instance,by formula

level=mean+coef·std,

wherein

mean=the average of the adjusted amplitude spectrum,

std=the standard deviation of the adjusted amplitude spectrum, and

coef=the coefficient determining the sensitivity of the cut level.

The cutting of the spectrum peaks is most preferably performed in twoparts; first the possible high peaks are removed 106 and then theremaining low peaks are removed 108. By performing the cutting in twoparts produces an exact and robust result. In addition, this ensuresthat partial discharges are not attenuated. In other words, the firstcutting 106 endeavours to remove the high peaks which strongly affectthe standard deviation, in which case, in the second cutting 108, thecut level level can be defined as exactly as possible above the bottomlevel. In the case of FIG. 10, for instance, an acceptable level valueof the second cutting could be between 5 and 15. Alternatively, it isalso possible to use only one cutting or more than two. If more than twocuttings were used, an even more exact and robust result would beachieved, but at the same time, the required calculation time wouldincrease. When using only one cutting, the effect of the high peaks onthe setting of the cut level is considerable. By a correct selection ofthe coefficient coef, it is possible to ensure that no filtering isdone, if no narrow-band interference exists in the signal. In such acase, the cut levels are set so high that the entire amplitude spectrumremains below them. When defining 105 the cut levels of high peaks, thevalue coef=4 is preferably used, and when defining 107 the cut levels oflow peaks, the value coef=3 is preferably used, when the cutting is donein two parts. Said values of the coefficient coef are based on testsperformed by the applicant. Other values can also be used, but the mostadvantageous value range of the coefficient coef when calculating thefirst cut level is 3 to 6 and when calculating the second cut level, itis 2 to 4.

Removing the spectrum peaks from the spectrum is done at the first stagein such a manner that the frequency ranges having values exceeding thehigh peaks cut level 61 in the adjusted amplitude spectrum |G(jω)|_(o)are nulled 106 in the spectrum G(jω). Because the spectrum G(jω) (likethe amplitude spectrum) is discrete in relation to the frequency, i.e.it is made up of frequency samples, the smallest frequency range whichcan be nulled is a frequency range of the length of one frequencysample. Narrow-band interference does typically not, however,concentrate exactly point by point on one frequency, but may be slightlymore widely spread. For instance, the bandwidth of an AM radiotransmission including sidebands may be 9 kHz. Thus, the interferencemay be spread in the area of several frequency samples. Because of this,it may be advantageous that the frequency range to be nulled comprisesnot only the frequency sample in which the interference shows, i.e. thecut level is exceeded, but also one or more neighbouring frequencysamples depending on the sampling frequency being used. For instance, ata 16 MHz sampling frequency and with a 960,000-point sample string, thewidth of one frequency sample corresponds to approximately 16.7 Hz. Ifthe width of the interference to be removed is 9 kHz, the number offrequency samples to be nulled is 540, i.e. 270 samples on both sides ofthe sample exceeding the cut level. The result is the corrected spectrumG(jω)₁. FIG. 6 shows an adjusted amplitude spectrum |G(jω)|_(o) and thefirst cut level 61. For defining 107 the cut level of low peaks, theamplitude values exceeding the cut level 61 are also nulled 106 in theadjusted amplitude spectrum, in which case block 107 uses the correctedadjusted amplitude spectrum |G(jω)|_(k) for defining the second cutlevel 71. The correction of the adjusted amplitude spectrum alters(makes smaller) its average and standard deviation, and the definitionof the second cut level can be made more accurately in the secondcutting. If more than two cuttings were used, the amplitude spectrumused in defining 105 and 107 the cut level would also be correctedcorrespondingly with each spectrum correction 106. The correction of theamplitude spectrum is not made with the last spectrum correction 108,because the amplitude spectrum will not be needed later. Removing 108low peaks from the spectrum is done correspondingly, i.e. the frequencyranges having values exceeding the cut level 71 of the low peaks in thecorrected adjusted amplitude spectrum |G(jω)|_(k) are nulled in thecorrected spectrum G(jω)₁. FIG. 7 shows the corrected adjusted amplitudespectrum and the second cut level 71. The result is thus the spectrumG(jω)₂ from which peaks have been removed at two different stages. Theamplitude spectrum |G(jω)₂| corresponding to this corrected spectrumG(jω)₂ is shown in FIG. 8. As a result of the filtering, low peakscaused by narrow-band interference often remain in the amplitudespectrum, but if the width and height of the peak is taken into account,it can be noted that the power of the interfering pulse is very low.FIG. 9 shows the corrected signal g(t)₂ in a time domain, obtained bytransforming 109 the twice corrected 106 and 108 spectrum, i.e. signal,G(jω)₂ from the frequency domain back to the time domain. Thenarrow-band interference remaining in the signal are considerably lowerthan the background noise, so distinguishing interference in the timedomain (FIG. 9) is almost impossible.

Eliminating asynchronous impulse interference 121

The operation of a method of eliminating asynchronous impulseinterference is based on the fact that the typical properties of apartial discharge pulse and asynchronous interfering pulses differ fromeach other enough to make distinguishing them possible. Partialdischarge pulses occur in pulse groups in areas of cycle depending onthe discharge type, and there is deviation in the location and amplitudeof a single pulse, whereas asynchronous impulse interference occurs atalmost equal intervals, at nearly a constant amplitude and during theentire cycle.

As starting data, the method uses pulse parameters obtained by findingthe pulses occurring in a partial discharge measuring signal andcalculating and storing the following information on them: amplitude,starting phase angle, cycle number, rise time, fall time, width and areaof the pulse. In the measurements used as examples, data was measuredduring three network cycles (60 ms with 50 Hz frequency) and in theearlier phases, the amplitude of the pulses was scaled to between 0 and128. No limits are set to the number of pulses. During one cycle, thereare assumed to be 5 to 500 asynchronous interfering pulses andconsequently, the time difference of consecutive interfering pulses isassumed to vary between 0.04 and 4 ms. The flow chart of the method isshown in FIG. 11.

At the pulse search and pulse parameter calculation stage 111, thepulses occurring in the measuring signal are found and pulse parameters(e.g. amplitude, phase angle and cycle number) are defined for thepulses. FIG. 14 shows a partial discharge measuring signal. The peaks ofthe pulses found in the signal have been circled. The pulse parametersof the found pulses are used as the starting data. FIG. 15 shows thepeaks of the found pulses in time order. In FIGS. 14 and 15, the X axisrepresents the samples, and thus in the case described herein, onesample corresponds to 62.5 ns. FIG. 15 also shows a 50 Hz sine wave toillustrate the location of the pulses in the phase voltage more clearly.The string of asynchronous interfering pulses is shown in FIG. 15 as astring in the amplitude area 20 to 25.

The amplitude area (0 to 128) is preferably divided (step 112) into 41examination periods, as shown in FIG. 12. The amplitude deviation, i.e.length of the period, is preferably ±3. The periods are examined 113 oneby one and only the impulses, whose amplitude is within the period, areexamined at each time. The amplitude area is divided into smallerexamination periods so as to be able to examine the time differencebetween pulses of certain amplitudes. By changing the amplitudedeviation and the number of examination periods, it is possible tochange the maximum allowed amplitude deviation of asynchronous impulseinterference. When the examination periods overlap somewhat, allasynchronous interferences can be detected regardless of their amplitudeand amplitude deviation.

By means of the information on the starting phase angle of the pulse andthe cycle number, it is possible to calculate the time difference ofequal-amplitude pulses to the pulses preceding them (for instance, thepulse peaks in FIG. 15 are set in place using the information on phaseangle and cycle number). The information is stored 114 in the timedifference matrix. FIG. 13 shows the basic structure of the matrix. Eachvertical line shows the time difference of the pulses in question to thepulses preceding them, for instance line 5, column 8 shows the timedifference of the fifth and eighth pulse. The time difference matrixcontains the time difference (i.e. distance) of each pulse to all pulsespreceding it within in the same examination period.

Simultaneously with making the time difference matrix, a time differencedistribution is created 114. The distribution is formed in the range of0.04 to 4 ms, for instance. The distribution is preferably divided into91 logarithmic blocks so that the step from one block to another is5.2%. At the location of the time difference in question, the value timedifference/20 is added to the distribution and, on both sides of thetime difference, the value (time difference/20)2. FIG. 16 shows thedivision of the blocks of the time difference distribution. The size ofthe blocks increases from left to right. The distribution is formed ofthe time difference between consecutive pulses in the same examinationperiod (i.e. the time difference of the pulse to the pulse precedingit). In practice, the values from which the distribution is formed areimmediately above the diagonal axis of the time difference matrix.

FIG. 17 shows a time difference distribution formed for the exemplarypartial discharge signal mentioned earlier in an examination period of21±3. The distribution shows a strong peak at approximately 1 ms and thepeak is higher than the value 1 set as the threshold, and consequentlyon the basis of the distribution, it can be noted that the signal hasasynchronous impulse interference at 1 ms (16,000 samples) intervals.

The value time difference/20 is a density index. The sum of indexeswhich is finally obtained from the distribution, depicts the seriousnessof the situation in a way. As the description will later show, so as tohave the pulses occurring at 0.2 ms intervals occur “as often” as thepulses occurring at 1 ms intervals, there must be five times as manypulses occurring at 0.2 ms intervals. The height of the peak in thedistribution shown in FIG. 17 is approximately 1.8. The peak value forthree cycles is 3, so the peak shows that over a half of the pulse pairsoccurring at 1 ms intervals have been detected. The time difference ofconsecutive pulses only is stored in the distribution.

The fact that the values (time difference/20)2 are added in thedistribution to neighbouring locations of the found time difference,endeavours to round the distribution and to ensure that also the timedifferences that are at the border of two blocks will be detected.

The value time difference/20 means that the value 1 will be obtained forthe distribution, if the cycle has 20 pulses at 1 ms intervals or 100pulses at 0.2 ms intervals. The distribution thus shows the timedifference at which interval equidistant pulses occur proportionally themost.

When the time difference information has been calculated for eachequal-amplitude pulse, the maximum of the time difference distributionis found 115 and the time difference with which it is realised. If thepeak value of the time difference distribution is higher than a pre-setthreshold value, pulses repeating at equal intervals in the timedifference matrix are searched for. A three-cycle long measurement usesthe value 1 as the threshold value. This requires that when forming thetime difference distribution, at least every third of the repeatingpulse pairs have been detected. The threshold value should not be settoo high, because it may happen that there are both partial dischargepulses and interfering pulses in the same examination period. It is notalways possible to obtain the time difference between two interferingpulses at partial discharge groups, but it is probable that a timedifference between a partial discharge pulse and an interfering pulse isobtained. The threshold value is, however, so high that partialdischarges do not exceed it. Not even the pulse strings of a coronadischarge are “long” enough to be interpreted as interference. The valuetime difference/20 scales the distribution so that the highest possiblevalue of the distribution corresponds to the number of cycles. If therewere samples from one network cycle only, the peak examined, and thepeak value of the distribution is 3.

Finding 116 pulses that repeat at equal intervals in the time differencematrix is done as follows. The search is started using pulse 1 as thefirst pulse. From the first horizontal line a value is searched which iswithin the range:

k·AE−0.135·AE≦Value≦k·AE+0.135·AE

wherein AE is the time difference between repeating pulses, obtainedfrom the time difference distribution and k is 1, 2, 3 . . . FIG. 20shows the values of a time difference matrix for the first 31 pulses.The time difference between repeating pulses is 1 ms in the example. Inthis case, the value meeting the conditions is found on line 1, location22 of the matrix. The pulse index is stored so that it can be marked asan interfering pulse, if the found pulse string meets the conditions setfor it. The pulse search is now continued from line 22. A value meetingthe conditions cannot be found in this table and it can be noted laterthat several pulses are missing from the found pulse string, i.e. it isnot uniform enough to be an interfering pulse string. The search is thenstarted from the beginning using pulse 2 as the first pulse. A valuemeeting the conditions is searched for on the 2^(nd) vertical line. Itis found at location 3. The pulse index is stored and the search iscontinued on line 3. A suitable value is found at location 5, the indexis stored and the search is continued on line 5. A value which is withinthe desired range when k=1 is searched for on line 5. Such a value isfound at location 7 and the search is continued on line 7. If the pulseat location 7 had not been found and the location in question had hadthe value 1.2 for instance, it would be noted here that when k=1, thereis no value meeting the conditions on this line, since the valuesincrease from left to right on the lines. The value of k would then beincreased by one and the search continued from this location onward. Avalue meeting this condition would be found in location 10 and thesearch continued on line 10. Always when starting to search for the nextinterfering pulse, the value of k is set to 1. If an interfering pulsecannot be found, the value of k is increased by one. When the timedifference of interfering pulses obtained from the distribution is 1 ms,for instance, a pulse string is searched for from the time differencematrix, whose time difference between pulses is k×1 ms±0.135 ms. Theinterfering pulses found in the exemplary signal are circled in FIG. 18.

When the entire pulse set has been examined, a check is made to seewhether the pulse string is sufficiently uniform. The condition ispreferably that at least half of the pulses have been found. If thepulse string is sufficiently uniform, the pulses in it are marked 117 asinterfering pulses. The number of found interfering pulses can easily becalculated and the maximum number of interfering pulses occurring atintervals of certain time differences can also be calculated by means ofthe time difference and the sampling time by dividing the sampling timeby the time difference between the pulses. In other words, in thethree-cycle long measurement and the 1-ms time difference used in theexample, this is (3×20 ms)/1 ms=60 pulses. Thus, if in this case, morethan 30 interfering pulses are found, the pulse string is sufficientlyuniform.

When all examination periods have been examined 118, all pulsesoccurring within the amplitude area are checked and the pulse parametersof the pulses that have been marked as interfering pulses are removed119 from the database. FIG. 19 shows an exemplary signal from which alldetected interfering pulses have been removed. The following parameters,for instance, can be changed in the method: amplitude deviation, numberof examination periods, time difference deviation and number of searchedasynchronous interfering pulses. Further, the time difference matrix canbe replaced by calculating only the values immediately above itsdiagonal, i.e. by defining the time difference of consecutive pulses.The time difference between two pulses can be calculated using thesevalues, but the calculation becomes more complicated.

Eliminating synchronous impulse interference 200

The operation of a method of eliminating synchronous impulseinterference is based on the fact that the typical properties of apartial discharge pulse and synchronous interfering pulses differ fromeach other enough to make distinguishing them possible. Partialdischarge pulses occur in pulse groups in areas of cycle depending onthe discharge type, but there is deviation in the location and amplitudeof a single pulse, whereas synchronous impulse interference occurs atnearly the same phase angle and at nearly constant amplitude.

As starting data, the method preferably uses pulse parameters obtainedby finding the pulses occurring in a partial discharge measuring signaland calculating and storing 120 the amplitude, starting phase angle,cycle number, rise time, fall time, width and area of the pulse. In theearlier phases, the amplitude of the pulses was scaled to between 0 and128. The number of synchronous interfering pulses during one cycle isassumed to vary case by case. The flow chart of the method is shown inFIG. 21.

The amplitude area (0 to 128) is divided (step 201) into smallerexamination periods like in connection with asynchronous impulseinterference elimination. The amplitude area is, for instance, dividedinto 20 partly overlapping examination periods and the width of a periodis ±0.055×the amplitude of the largest pulse in the pulse series. Theexamination periods are examined 202 one at a time and only the pulsesare examined, whose amplitude is within the period.

First a phase angle distribution of the pulses is formed 203 by checkingall pulses in the same examination period and forming the distributionfrom their starting phase angles. The distribution is divided into 180blocks, for instance, in relation to the phase, i.e. the width of oneblock corresponds to 2°. When forming the distribution, one should alsotake into consideration the other pulses occurring in the same cycle.Forming the distribution is started from the first measured cycle andthe first block of the distribution, i.e. phase angles 0 to 2°. Thevalue 1 is added to the first block of the distribution, if in the firstcycle, only one pulse occurs in said block and no pulses occur withintwo blocks of the examined block, i.e. phase angles 2 to 6°. If thefifth block, for instance, were examined, i.e. phase angles 8 to 10°,there should be no pulses at the phase angles 4 to 8° and 10 to 14°. Ifthere are more than one pulse in the block or if there are pulses in theneighbouring blocks, the value of the distribution is not changed. Thisaction endeavours to prevent the elimination of partial dischargepulses. Even though partial discharge pulses are quite irregular, it ispossible that, in the middle of pulse groups, pulses occur so denselythat some of them may be identified as synchronous impulse interference.The drawback in this is that synchronous interfering pulses occurring atthe same phase angles as partial discharges cannot necessarily beidentified. The obtained distribution is rounded by summing the valuesof the neighbouring blocks to each block.

Before finding the peaks 204, the phase angle distribution is normed bydividing its values by the number of cycles. Any values higher than 0.4are interpreted 204 as peaks of the phase angle distribution. If peaksare detected, the pulses are re-examined and the pulses occurring atphase angles corresponding to the peaks are marked 205 as interferingpulses. The marking as interfering pulses is, however, done so that onlyone pulse per cycle is marked as an interfering pulse in one phasewindow. Thus, if one phase window has both a partial discharge pulse andan interfering pulse, at least one of them will be analysed.

When all examination periods have been examined 206, all pulses withinthe amplitude area are checked and the pulse parameters of the pulsesthat are marked as interfering pulses are removed 207 from the database.In the method, the following parameters, for instance, can be changed:amplitude deviation, number of examination periods and threshold valueof peak search.

Analysing partial discharges 300

Measurements are made until there is at least a certain number 122 ofcollected pulses which herein is called the analysis limit. The analysislimit is a thousand pulses, for instance. A collected group of forinstance a thousand pulses is in the following called a sample.

One or more characteristic parameters are defined 301 of the collectedpulses. The characteristic parameters may correspond to the pulseparameters defined 400 earlier from the pulses, in which case the valuesof the corresponding pulse parameters are defined as the values of thecharacteristic parameters. According to a preferred embodiment of theinvention, the following three characteristic parameters are defined:

Characteristic parameter Tr (rise time) shows the rise time of the pulsemeasured from the rise side of the pulse, for instance between the 10%and 90% points.

Characteristic parameter Tf (fall time) shows the fall time of the pulsemeasured from the fall side of the pulse, for instance between the 90%and 10% points.

Characteristic parameter Tw (width of the pulse) shows the width of thepulse measured from between the rise and fall sides of the pulse, forinstance between the 50% points.

It should be noted that other characteristic parameters can also be usedwithout this bearing any significance to the basic idea of theinvention.

When the sample has been collected, one or more histograms are formed302 from the pulses on the basis of the characteristic parameters.According to a preferred embodiment, a rise time histogram is firstformed. FIG. 24 shows an example of a rise time histogram. The rise timeis shown on the horizontal axis of the rise time histogram divided intocertain intervals and the number of pulses in each interval is shown onthe vertical axis. The rise time histogram is preferably filtered with aFIR (finite impulse response) filter of the eighth order, whose barrierfrequency is suitably selected. The barrier frequency can be 0.1×f_(N),for instance. The frequency f_(N) is a Nyquist frequency, i.e. half ofthe sampling frequency. In this case, the histogram is processed as asample string whose sampling frequency is 2 Hz, for instance, i.e. f_(N)becomes 1 Hz and the barrier frequency of the filter 0.1 Hz. FIG. 25shows an example of a filtered rise time histogram. The histogram isdivided into classes on the basis of minimum local values between peaksin such a manner, for instance, that first the highest value of thefiltered histogram is searched for, which in FIG. 25 is at 50 ns. Afterthis, peaks which are higher than 0.5×the highest value, for instance,are searched for. In the case of FIG. 25, a peak meeting the conditionis at approximately 150 ns. The smallest value between peaks is definedas the minimum value between peaks, this being at 100 ns in FIG. 25. Thehighest value of the histogram is thus set as the first peak and, as thenext peaks, the ones whose height is at least the set limit value (e.g.0.2 to 0.5×the highest peak). A peak refers to a value (peak) which ishigher than the values surrounding it. A peak can also extend over morethan one interval. When defining the total number, the absolute locationof the peak is not significant, the important thing is to find theminimum value between two peaks. In the case of FIG. 25, division intotwo groups would be made in such a manner that the first group would bemade up of pulses whose rise time is 100 ns, and the second group wouldbe made up of pulses whose rise time is >100 ns. Fall time histogramswould further be formed for each pulse group obtained in this manner.The histograms are filtered with the above-mentioned FIR filter anddivided into groups on the basis of the minimum values between peaks, asdescribed above. Separate pulse groups are formed of pulses belonging todifferent groups. The pulse groups formed on the basis of the rise timehistogram are thus divided further into subgroups, if the main groupshave pulses with different fall times. Pulse width histograms are thenformed of each pulse group formed on the basis of the rise and fall timehistograms. The histograms are filtered with the above-mentioned FIRfilter and divided into groups on the basis of the minimum valuesbetween peaks, as described above. Separate pulse groups are formed ofpulses belonging to different groups. The pulse groups formed on thebasis of the rise and fall time histograms are thus divided intosubgroups, if the groups have pulses with different pulse widths. Thesize of the interval in the histograms is bound to the sampling intervaltS (e.g. 62.5 ns) of the measurement system so that the interval of therise time histogram is preferably 0.4×t_(S) (e.g. 25 ns), that of thefall time histogram is correspondingly t_(S) (e.g. 62.5 ns) and theinterval of the pulse width histogram is 0.5×t_(S) (e.g. 31.25 ns). Itmay be necessary to alter the values of the coefficients, if the form ofthe pulses being measured or the properties of the measurement systemdiffer considerably from the examples used in this description.

After this preferably three-stage division, each pulse group onlycontains the pulses of one or more discharge sources located at acertain distance from the measuring point. Thus, the number of pulsegroups is obtained 305 as the number of discharge sources at differentdistances from the measuring point. In addition, partial dischargesources of different type located at the same distance aredistinguished, if their pulse forms differ substantially from eachother. Otherwise, partial discharge sources located at the same distancecan be identified and distinguished 305 either manually (by examiningthe discharge pulse groups visually) or automatically by using anidentification method of partial discharge types.

It is obvious to a person skilled in the art that while technologyadvances, the basic idea of the invention can be implemented in manydifferent ways. The invention and its embodiments are thus notrestricted to the examples described above, but can vary within thescope of the claims.

What is claimed is:
 1. A method of separating partial discharge pulsesoriginating from different partial discharge sources in an electricsystem, which method comprises: measuring a variable of the electricsystem, such as voltage or current, to which partial dischargesoccurring in the electric system cause pulses, separating the pulsescaused by partial discharges occurring in the measured variable,defining and storing pulse parameters depicting the pulse form of thepartial discharge pulses, or information from which the pulse parametersdepicting the pulse form can be derived, defining one or morecharacteristic parameters depicting the properties of partial dischargepulses for each partial discharge pulse by means of the pulse parametersafter at least a predefined number of partial discharge pulses has beenobtained, and dividing the partial discharge pulses originating fromdifferent partial discharge sources into pulse groups on the basis ofsaid one or more values of characteristic parameters.
 2. A method asclaimed in claim 1, wherein the method also comprises defining thenumber of the partial discharge sources on the basis of the number ofpulse groups obtained during the dividing the partial discharge pulsesinto pulse groups.
 3. A method as claimed in claim 1, wherein dividingthe partial discharge pulses into pulse groups, when at least a firstcharacteristic parameter has been defined for the partial dischargepulses, comprises: forming a histogram of the pulses on the basis of thefirst characteristic parameter defined from each pulse, dividing thehistogram into classes on the basis of the minimum local values betweenpeaks exceeding a certain predefined limit value, and dividing thepulses into groups so that the pulses belonging to the same histogramclass belong to the same pulse group.
 4. A method as claimed in claim 3,wherein the dividing the partial discharge pulses into pulse groups,when at least a first and second characteristic parameter have beendefined for the partial discharge pulses, also comprises: forming on thebasis of the second characteristic parameter histograms of the pulsegroups formed on the basis of the first characteristic parameter,dividing the histograms formed on the basis of the second characteristicparameter into classes on the basis of the minimum local values betweenpeaks exceeding a certain predefined limit value, and re-dividing thepulses into groups so that the pulses belonging to the same histogramclass belong to the same pulse group.
 5. A method as claimed in claim 4,wherein the dividing the partial discharge pulses into pulse groups,when at least a first, a second and a third characteristic parameterhave been defined for the partial discharge pulses, also comprises:forming on the basis of the third characteristic parameter histograms ofthe pulse groups formed on the basis of the second characteristicparameter, dividing the histograms formed on the basis of the thirdcharacteristic parameter into classes on the basis of the minimum localvalues between peaks exceeding a certain predefined limit value, andre-dividing the pulses into groups so that the pulses belonging to thesame histogram class belong to the same pulse group.
 6. A method asclaimed in claim 5, wherein the following three characteristicparameters are defined for each partial discharge pulse: rise time Trshowing the rise time of the pulse, fall time Tf showing the fall timeof the pulse, and pulse width Tw showing the width of the pulse.
 7. Amethod as claimed in claim 3 wherein the envelope of the histogram isdefined prior to division into classes.
 8. A method as claimed in claim5, wherein the envelope of the histogram is defined prior to divisioninto classes.
 9. A method as claimed in claim 1, wherein the dividingthe partial discharge pulses into pulse groups, when at least a firstand second characteristic parameter have been defined for the partialdischarge pulses, also comprises: forming histograms of the pulses onthe basis of each characteristic parameter defined from a pulse,dividing the histograms into classes on the basis of the minimum localvalues between peaks exceeding a certain predefined limit value, anddividing the pulses into groups so that the pulses belonging to the samegroups in all histograms belong to the same pulse group.
 10. A method asclaimed in claim 9, wherein the following three characteristicparameters are defined for each partial discharge pulse: rise time Trshowing the rise time of the pulse, fall time Tf showing the fall timeof the pulse, and pulse width Tw showing the width of the pulse.
 11. Amethod as claimed in claim 9 wherein the envelope of the histogram isdefined prior to division into classes.
 12. An arrangement forseparating partial discharge pulses originating from different partialdischarge sources in an electric system, which arrangement comprises:measuring means arranged to: measure a variable of the electric system,such as voltage or current, to which partial discharges occurring in theelectric system cause pulses, separate the pulses caused by partialdischarges occurring in the measured variable, define and store pulseparameters depicting the pulse form of the partial discharge pulses, orinformation from which the pulse parameters depicting the pulse form canbe derived, and analysing means arranged to define one or morecharacteristic parameters depicting the properties of partial dischargepulses for each partial discharge pulse by means of the pulse parametersafter at least a predefined number of partial discharge pulses has beenobtained, wherein the analysing means are also arranged to divide thepartial discharge pulses originating from different partial dischargesources into pulse groups on the basis of said one or more values ofcharacteristic parameters.
 13. An arrangement as claimed in claim 12,wherein the analysing means are also arranged to define the number ofthe partial discharge sources on the basis of the number of pulse groupsobtained by means of said division.
 14. An arrangement as claimed inclaim 12, wherein the analysing means, when dividing the partialdischarge pulses into pulse groups and when at least a firstcharacteristic parameter has been defined for the partial dischargepulses, are arranged to form a histogram of the pulses on the basis ofthe first characteristic parameter defined from each pulse, divide thehistogram into classes on the basis of the minimum local values of peaksexceeding a certain predefined limit value, and divide the pulses intogroups so that the pulses belonging to the same histogram class belongto the same pulse group.
 15. An arrangement as claimed in claim 14,wherein the analysing means when dividing the partial discharge pulsesinto pulse groups and when at least a first and a second characteristicparameter, have been defined for the partial discharge pulses, are alsoarranged to form on the basis of the second characteristic parameterhistograms of the pulse groups formed on the basis of the firstcharacteristic parameter, divide the histograms formed on the basis ofthe second characteristic parameter into classes on the basis of theminimum local values between peaks exceeding a certain predefined limitvalue, and re-divide the pulses into groups so that the pulses belongingto the same histogram class belong to the same pulse group.
 16. Anarrangement as claimed in claim 15, wherein the analysing means, whendividing the partial discharge pulses into pulse groups and when atleast three characteristic parameters, i.e. a first, a second and athird characteristic parameter, have been defined for the partialdischarge pulses, are also arranged to form on the basis of the thirdcharacteristic parameter histograms of the pulses formed on the basis ofthe second characteristic parameter, divide the histograms formed on thebasis of the third characteristic parameter into classes on the basis ofthe minimum local values between peaks exceeding a certain predefinedlimit value, and re-divide the pulses into groups so that the pulsesbelonging to the same histogram class belong to the same pulse group.17. An arrangement as claimed in claim 16, wherein the analysing meansare arranged to define the following three characteristic parameters foreach partial discharge pulse: rise time Tr showing the rise time of thepulse, fall time Tf showing the fall time of the pulse, and pulse widthTw showing the width of the pulse.
 18. An arrangement as claimed inclaim 14, wherein the analysing means are arranged to define theenvelope of the histogram prior to division into classes.
 19. Anarrangement as claimed in claim 16, wherein the analysing means arearranged to define the envelope of the histogram prior to division intoclasses.
 20. An arrangement as claimed in claim 12, wherein theanalysing means, when dividing the partial discharge pulses into pulsegroups and when at least two characteristic parameters have been definedfor the partial discharge pulses, are also arranged to form histogramsof the pulses on the basis of each characteristic parameter defined froma pulse, divide the histograms into classes on the basis of the minimumlocal values between peaks exceeding a certain predefined limit value,and re-divide the pulses into groups so that the pulses belonging to thesame classes in all histograms belong to the same pulse group.
 21. Anarrangement as claimed in claim 20, wherein the analysing means arearranged to define the following three characteristic parameters foreach partial discharge pulse: rise time Tr showing the rise time of thepulse, fall time Tf showing the fall time of the pulse, and pulse widthTw showing the width of the pulse.
 22. An arrangement as claimed inclaim 20, wherein the analysing means are arranged to define theenvelope of the histogram prior to division into classes.